Density curves : Modeling data distributions Normal distributions and the empirical rule : Modeling data distributions Normal distribution calculations : Modeling data distributions More on normal distributions : Modeling data distributions.
What you'll learn
Exploring bivariate numerical data. Introduction to scatterplots : Exploring bivariate numerical data Correlation coefficients : Exploring bivariate numerical data Introduction to trend lines : Exploring bivariate numerical data. Least-squares regression equations : Exploring bivariate numerical data Assessing the fit in least-squares regression : Exploring bivariate numerical data More on regression : Exploring bivariate numerical data. Study design. Statistical questions : Study design Sampling and observational studies : Study design Sampling methods : Study design.
Combinatorial Inference in Geometric Data Analysis - CRC Press Book
Types of studies experimental vs. Basic theoretical probability : Probability Probability using sample spaces : Probability Basic set operations : Probability Experimental probability : Probability. Randomness, probability, and simulation : Probability Addition rule : Probability Multiplication rule for independent events : Probability Multiplication rule for dependent events : Probability Conditional probability and independence : Probability.
Counting, permutations, and combinations.
Counting principle and factorial : Counting, permutations, and combinations Permutations : Counting, permutations, and combinations Combinations : Counting, permutations, and combinations. Combinatorics and probability : Counting, permutations, and combinations. Random variables. Discrete random variables : Random variables Continuous random variables : Random variables Transforming random variables : Random variables Combining random variables : Random variables. Binomial random variables : Random variables Binomial mean and standard deviation formulas : Random variables Geometric random variables : Random variables More on expected value : Random variables Poisson distribution : Random variables.
Sampling distributions. What is a sampling distribution?
Harvard Online Courses
Confidence intervals. Introduction to confidence intervals : Confidence intervals Estimating a population proportion : Confidence intervals Estimating a population mean : Confidence intervals.
- The Voyage of the Beagle;
- Introduction to R!
- RPubs - statistical-inference:Part 2-Basic Inferential Data Analysis Instructionsless.
More confidence interval videos : Confidence intervals. Significance tests hypothesis testing. The idea of significance tests : Significance tests hypothesis testing Error probabilities and power : Significance tests hypothesis testing Tests about a population proportion : Significance tests hypothesis testing. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast.
- The gaucho Martín Fierro.
- Explore Library of Case Studies!
- Causation and free will!
- Data analysis and statistical inference : A quick guide.
Once you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the election.
Conoce a tus instructores Harvard University. Rafael Irizarry Professor of Biostatistics. Preguntas frecuentes Honor code statement HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant.
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